The feasibility of hybrid solar-biomass power plants in India

We assess the feasibility of hybrid solar-biomass power plants for use in India in various applications including tri-generation, electricity generation and process heat. To cover this breadth of scenarios we analyse, with the help of simulation models, case studies with peak thermal capacities ranging from 2 to 10 MW. Evaluations are made against technical, financial and environmental criteria. Suitable solar multiples, based on the trade-offs among the various criteria, range from 1 to 2.5. Compared to conventional energy sources, levelised energy costs are high - but competitive in comparison to other renewables such as photovoltaic and wind. Long payback periods for hybrid plants mean that they cannot compete directly with biomass-only systems. However, a 1.2-3.2 times increase in feedstock price will result in hybrid systems becoming cost competitive. Furthermore, in comparison to biomass-only, hybrid operation saves up to 29% biomass and land with an 8.3-24.8 $/GJ/a and 1.8-5.2 ¢/kWh increase in cost per exergy loss and levelised energy cost. Hybrid plants will become an increasingly attractive option as the cost of solar thermal falls and feedstock, fossil fuel and land prices continue to rise. In the foreseeable future, solar will continue to rely on subsidies and it is recommended to subsidise preferentially tri-generation plants. © 2012 Elsevier Ltd.

Vertical and horizontal scope economies in the regulated US electricity power industry

This paper investigates vertical economies between generation and distribution of electric power, and horizontal economies between different types of power generation in the U.S. electric utility industry. Our quadratic cost function model includes three generation output measures (hydro, nuclear and fossil fuels), which allows us to analyze the effect that generation mix has on vertical economies. Our results provide (sample mean) estimates of vertical economies of 8.1% and horizontal economies of 5.4%. An extensive sensitivity analysis is used to show how the scope measures vary across alternative model specifications and firm types. © 2012 Blackwell Publishing Ltd and the Editorial Board of The Journal of Industrial Economics.

A techno-economic comparison of power production by biomass fast pyrolysis with gasification and combustion

This paper presents an assessment of the technical and economic performance of thermal processes to generate electricity from a wood chip feedstock by combustion, gasification and fast pyrolysis. The scope of the work begins with the delivery of a wood chip feedstock at a conversion plant and ends with the supply of electricity to the grid, incorporating wood chip preparation, thermal conversion, and electricity generation in dual fuel diesel engines. Net generating capacities of 1–20 MWe are evaluated. The techno-economic assessment is achieved through the development of a suite of models that are combined to give cost and performance data for the integrated system. The models include feed pretreatment, combustion, atmospheric and pressure gasification, fast pyrolysis with pyrolysis liquid storage and transport (an optional step in de-coupled systems) and diesel engine or turbine power generation. The models calculate system efficiencies, capital costs and production costs. An identical methodology is applied in the development of all the models so that all of the results are directly comparable. The electricity production costs have been calculated for 10th plant systems, indicating the costs that are achievable in the medium term after the high initial costs associated with novel technologies have reduced. The costs converge at the larger scale with the mean electricity price paid in the EU by a large consumer, and there is therefore potential for fast pyrolysis and diesel engine systems to sell electricity directly to large consumers or for on-site generation. However, competition will be fierce at all capacities since electricity production costs vary only slightly between the four biomass to electricity systems that are evaluated. Systems de-coupling is one way that the fast pyrolysis and diesel engine system can distinguish itself from the other conversion technologies. Evaluations in this work show that situations requiring several remote generators are much better served by a large fast pyrolysis plant that supplies fuel to de-coupled diesel engines than by constructing an entire close-coupled system at each generating site. Another advantage of de-coupling is that the fast pyrolysis conversion step and the diesel engine generation step can operate independently, with intermediate storage of the fast pyrolysis liquid fuel, increasing overall reliability. Peak load or seasonal power requirements would also benefit from de-coupling since a small fast pyrolysis plant could operate continuously to produce fuel that is stored for use in the engine on demand. Current electricity production costs for a fast pyrolysis and diesel engine system are 0.091/kWh at 1 MWe when learning effects are included. These systems are handicapped by the typical characteristics of a novel technology: high capital cost, high labour, and low reliability. As such the more established combustion and steam cycle produces lower cost electricity under current conditions. The fast pyrolysis and diesel engine system is a low capital cost option but it also suffers from relatively low system efficiency particularly at high capacities. This low efficiency is the result of a low conversion efficiency of feed energy into the pyrolysis liquid, because of the energy in the char by-product. A sensitivity analysis has highlighted the high impact on electricity production costs of the fast pyrolysis liquids yield. The liquids yield should be set realistically during design, and it should be maintained in practice by careful attention to plant operation and feed quality. Another problem is the high power consumption during feedstock grinding. Efficiencies may be enhanced in ablative fast pyrolysis which can tolerate a chipped feedstock. This has yet to be demonstrated at commercial scale. In summary, the fast pyrolysis and diesel engine system has great potential to generate electricity at a profit in the long term, and at a lower cost than any other biomass to electricity system at small scale. This future viability can only be achieved through the construction of early plant that could, in the short term, be more expensive than the combustion alternative. Profitability in the short term can best be achieved by exploiting niches in the market place and specific features of fast pyrolysis. These include: •countries or regions with fiscal incentives for renewable energy such as premium electricity prices or capital grants; •locations with high electricity prices so that electricity can be sold direct to large consumers or generated on-site by companies who wish to reduce their consumption from the grid; •waste disposal opportunities where feedstocks can attract a gate fee rather than incur a cost; •the ability to store fast pyrolysis liquids as a buffer against shutdowns or as a fuel for peak-load generating plant; •de-coupling opportunities where a large, single pyrolysis plant supplies fuel to several small and remote generators; •small-scale combined heat and power opportunities; •sales of the excess char, although a market has yet to be established for this by-product; and •potential co-production of speciality chemicals and fuel for power generation in fast pyrolysis systems.

Introduction to the Maple Power Tool Intpakx

The paper has been presented at the 12th International Conference on Applications of Computer Algebra, Varna, Bulgaria, June, 2006

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.

Short-term wind power forecasting using Gaussian processes

Since wind has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safety and economics of wind energy utilization. In this paper, we investigate a combination of numeric and probabilistic models: one-day-ahead wind power forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical Weather Prediction (NWP) model. Firstly the wind speed data from NWP was corrected by a GP. Then, as there is always a defined limit on power generated in a wind turbine due the turbine controlling strategy, a Censored GP was used to model the relationship between the corrected wind speed and power output. To validate the proposed approach, two real world datasets were used for model construction and testing. The simulation results were compared with the persistence method and Artificial Neural Networks (ANNs); the proposed model achieves about 11% improvement in forecasting accuracy (Mean Absolute Error) compared to the ANN model on one dataset, and nearly 5% improvement on another.

Optimal investment under stochastic volatility and power type utility function

2000 Mathematics Subject Classification: 37F21, 70H20, 37L40, 37C40, 91G80, 93E20.

Surface Mean Flow and Turbulence Structure in Tropical Cyclone Winds

Hurricanes are one of the deadliest and costliest natural hazards affecting the Gulf coast and Atlantic coast areas of the United States. An effective way to minimize hurricane damage is to strengthen structures and buildings. The investigation of surface level hurricane wind behavior and the resultant wind loads on structures is aimed at providing structural engineers with information on hurricane wind characteristics required for the design of safe structures. Information on mean wind profiles, gust factors, turbulence intensity, integral scale, and turbulence spectra and co-spectra is essential for developing realistic models of wind pressure and wind loads on structures. The research performed for this study was motivated by the fact that considerably fewer data and validated models are available for tropical than for extratropical storms. Using the surface wind measurements collected by the Florida Coastal Monitoring Program (FCMP) during hurricane passages over coastal areas, this study presents comparisons of surface roughness length estimates obtained by using several estimation methods, and estimates of the mean wind and turbulence structure of hurricane winds over coastal areas under neutral stratification conditions. In addition, a program has been developed and tested to systematically analyze Wall of Wind (WoW) data, that will make it possible to perform analyses of baseline characteristics of flow obtained in the WoW. This program can be used in future research to compare WoW data with FCMP data, as gust and turbulence generator systems and other flow management devices will be used to create WoW flows that match as closely as possible real hurricane wind conditions. Hurricanes are defined as tropical cyclones for which the maximum 1-minute sustained surface wind speeds exceed 74 mph. FCMP data include data for tropical cyclones with lower sustained speeds. However, for the winds analyzed in this study the speeds were sufficiently high to assure that neutral stratification prevailed. This assures that the characteristics of those winds are similar to those prevailing in hurricanes. For this reason in this study the terms tropical cyclones and hurricanes are used interchangeably.

The critical role of intracavity dynamics in high-power mode-locked fiber lasers

We present a theoretical description of the generation of ultra-short, high-energy pulses in two laser cavities driven by periodic spectral filtering or dispersion management. Critical in driving the intra-cavity dynamics is the nontrivial phase profiles generated and their periodic modification from either spectral filtering or dispersion management. For laser cavities with a spectral filter, the theory gives a simple geometrical description of the intra-cavity dynamics and provides a simple and efficient method for optimizing the laser cavity performance. In the dispersion managed cavity, analysis shows the generated self-similar behavior to be governed by the porous media equation with a rapidly-varying, mean-zero diffusion coefficient whose solution is the well-known Barenblatt similarity solution with parabolic profile. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Intracavity dynamics for power and energy enhancement in mode-locked lasers

We present a theoretical description of the generation of ultra-short, high-energy pulses in two laser cavities driven by periodic spectral filtering or dispersion management. Critical in driving the intra-cavity dynamics is the nontrivial phase profiles generated and their periodic modification from either spectral filtering or dispersion management. For laser cavities with a spectral filter, the theory gives a simple geometrical description of the intra-cavity dynamics and provides a simple and efficient method for optimizing the laser cavity performance. In the dispersion managed cavity, analysis shows the generated self-similar behavior to be governed by the porous media equation with a rapidly-varying, mean-zero diffusion coefficient whose solution is the well-known Barenblatt similarity solution with parabolic profile.

Investigation of factors driving the costs of operating the 2020 Irish power system with large-scale wind generation

The main goal of this work is to determine the true cost incurred by the Republic of Ireland and Northern Ireland in order to meet their EU renewable electricity targets. The primary all-island of Ireland policy goal is that 40% of electricity will come from renewable sources in 2020. From this it is expected that wind generation on the Irish electricity system will be in the region of 32-37% of total generation. This leads to issues resulting from wind energy being a non-synchronous, unpredictable and variable source of energy use on a scale never seen before for a single synchronous system. If changes are not made to traditional operational practices, the efficient running of the electricity system will be directly affected by these issues in the coming years. Using models of the electricity system for the all-island grid of Ireland, the effects of high wind energy penetration expected to be present in 2020 are examined. These models were developed using a unit commitment, economic dispatch tool called PLEXOS which allows for a detailed representation of the electricity system to be achieved down to individual generator level. These models replicate the true running of the electricity system through use of day-ahead scheduling and semi-relaxed use of these schedules that reflects the Transmission System Operator's of real time decision making on dispatch. In addition, it carefully considers other non-wind priority dispatch generation technologies that have an effect on the overall system. In the models developed, three main issues associated with wind energy integration were selected to be examined in detail to determine the sensitivity of assumptions presented in other studies. These three issues include wind energy's non-synchronous nature, its variability and spatial correlation, and its unpredictability. This leads to an examination of the effects in three areas: the need for system operation constraints required for system security; different onshore to offshore ratios of installed wind energy; and the degrees of accuracy in wind energy forecasting. Each of these areas directly impact the way in which the electricity system is run as they address each of the three issues associated with wind energy stated above, respectively. It is shown that assumptions in these three areas have a large effect on the results in terms of total generation costs, wind curtailment and generator technology type dispatch. In particular accounting for these issues has resulted in wind curtailment being predicted in much larger quantities than had been previously reported. This would have a large effect on wind energy companies because it is already a very low profit margin industry. Results from this work have shown that the relaxation of system operation constraints is crucial to the economic running of the electricity system with large improvements shown in the reduction of wind curtailment and system generation costs. There are clear benefits in having a proportion of the wind installed offshore in Ireland which would help to reduce variability of wind energy generation on the system and therefore reduce wind curtailment. With envisaged future improvements in day-ahead wind forecasting from 8% to 4% mean absolute error, there are potential reductions in wind curtailment system costs and open cycle gas turbine usage. This work illustrates the consequences of assumptions in the areas of system operation constraints, onshore/offshore installed wind capacities and accuracy in wind forecasting to better inform the true costs associated with running Ireland's changing electricity system as it continues to decarbonise into the near future. This work also proposes to illustrate, through the use of Ireland as a case study, the effects that will become ever more prevalent in other synchronous systems as they pursue a path of increasing renewable energy generation.

Estimation of Sample Size and Power For Quantile Regression

Quantile regression (QR) was first introduced by Roger Koenker and Gilbert Bassett in 1978. It is robust to outliers which affect least squares estimator on a large scale in linear regression. Instead of modeling mean of the response, QR provides an alternative way to model the relationship between quantiles of the response and covariates. Therefore, QR can be widely used to solve problems in econometrics, environmental sciences and health sciences. Sample size is an important factor in the planning stage of experimental design and observational studies. In ordinary linear regression, sample size may be determined based on either precision analysis or power analysis with closed form formulas. There are also methods that calculate sample size based on precision analysis for QR like C.Jennen-Steinmetz and S.Wellek (2005). A method to estimate sample size for QR based on power analysis was proposed by Shao and Wang (2009). In this paper, a new method is proposed to calculate sample size based on power analysis under hypothesis test of covariate effects. Even though error distribution assumption is not necessary for QR analysis itself, researchers have to make assumptions of error distribution and covariate structure in the planning stage of a study to obtain a reasonable estimate of sample size. In this project, both parametric and nonparametric methods are provided to estimate error distribution. Since the method proposed can be implemented in R, user is able to choose either parametric distribution or nonparametric kernel density estimation for error distribution. User also needs to specify the covariate structure and effect size to carry out sample size and power calculation. The performance of the method proposed is further evaluated using numerical simulation. The results suggest that the sample sizes obtained from our method provide empirical powers that are closed to the nominal power level, for example, 80%.

Designing Short Term Wave Traces to Assess Wave Power Devices

In recent years modern numerical methods have been employed in the design of Wave Energy Converters (WECs), however the high computational costs associated with their use makes it prohibitive to undertake simulations involving statistically relevant numbers of wave cycles. Experimental tests in wave tanks could also be performed more efficiently and economically if short time traces, consisting of only a few wave cycles, could be used to evaluate the hydrodynamic characteristics of a particular device or design modification. Ideally, accurate estimations of device performance could be made utilizing results obtained from investigations with a relatively small number of wave cycles. However the difficulty here is that many WECs, such as the Oscillating Wave Surge Converter (OWSC), exhibit significant non-linearity in their response. Thus it is challenging to make accurate predictions of annual energy yield for a given spectral sea state using short duration realisations of that sea. This is because the non-linear device response to particular phase couplings of sinusoidal components within those time traces might influence the estimate of mean power capture obtained. As a result it is generally accepted that the most appropriate estimate of mean power capture for a sea state be obtained over many hundreds (or thousands) of wave cycles. This ensures that the potential influence of phase locking is negligible in comparison to the predictions made. In this paper, potential methods of providing reasonable estimates of relative variations in device performance using short duration sea states are introduced. The aim of the work is to establish the shortness of sea state required to provide statistically significant estimations of the mean power capture of a particular type of Wave Energy Converter. The results show that carefully selected wave traces can be used to reliably assess variations in power output due to changes in the hydrodynamic design or wave climate. 

Exergetic, energetic, economic and environmental evaluation of concentrated solar power plants in Libya

The PhD project addresses the potential of using concentrating solar power (CSP) plants as a viable alternative energy producing system in Libya. Exergetic, energetic, economic and environmental analyses are carried out for a particular type of CSP plants. The study, although it aims a particular type of CSP plant – 50 MW parabolic trough-CSP plant, it is sufficiently general to be applied to other configurations. The novelty of the study, in addition to modeling and analyzing the selected configuration, lies in the use of a state-of-the-art exergetic analysis combined with the Life Cycle Assessment (LCA). The modeling and simulation of the plant is carried out in chapter three and they are conducted into two parts, namely: power cycle and solar field. The computer model developed for the analysis of the plant is based on algebraic equations describing the power cycle and the solar field. The model was solved using the Engineering Equation Solver (EES) software; and is designed to define the properties at each state point of the plant and then, sequentially, to determine energy, efficiency and irreversibility for each component. The developed model has the potential of using in the preliminary design of CSPs and, in particular, for the configuration of the solar field based on existing commercial plants. Moreover, it has the ability of analyzing the energetic, economic and environmental feasibility of using CSPs in different regions of the world, which is illustrated for the Libyan region in this study. The overall feasibility scenario is completed through an hourly analysis on an annual basis in chapter Four. This analysis allows the comparison of different systems and, eventually, a particular selection, and it includes both the economic and energetic components using the “greenius” software. The analysis also examined the impact of project financing and incentives on the cost of energy. The main technological finding of this analysis is higher performance and lower levelized cost of electricity (LCE) for Libya as compared to Southern Europe (Spain). Therefore, Libya has the potential of becoming attractive for the establishment of CSPs in its territory and, in this way, to facilitate the target of several European initiatives that aim to import electricity generated by renewable sources from North African and Middle East countries. The analysis is presented a brief review of the current cost of energy and the potential of reducing the cost from parabolic trough- CSP plant. Exergetic and environmental life cycle assessment analyses are conducted for the selected plant in chapter Five; the objectives are 1) to assess the environmental impact and cost, in terms of exergy of the life cycle of the plant; 2) to find out the points of weakness in terms of irreversibility of the process; and 3) to verify whether solar power plants can reduce environmental impact and the cost of electricity generation by comparing them with fossil fuel plants, in particular, Natural Gas Combined Cycle (NGCC) plant and oil thermal power plant. The analysis also targets a thermoeconomic analysis using the specific exergy costing (SPECO) method to evaluate the level of the cost caused by exergy destruction. The main technological findings are that the most important contribution impact lies with the solar field, which reports a value of 79%; and the materials with the vi highest impact are: steel (47%), molten salt (25%) and synthetic oil (21%). The “Human Health” damage category presents the highest impact (69%) followed by the “Resource” damage category (24%). In addition, the highest exergy demand is linked to the steel (47%); and there is a considerable exergetic demand related to the molten salt and synthetic oil with values of 25% and 19%, respectively. Finally, in the comparison with fossil fuel power plants (NGCC and Oil), the CSP plant presents the lowest environmental impact, while the worst environmental performance is reported to the oil power plant followed by NGCC plant. The solar field presents the largest value of cost rate, where the boiler is a component with the highest cost rate among the power cycle components. The thermal storage allows the CSP plants to overcome solar irradiation transients, to respond to electricity demand independent of weather conditions, and to extend electricity production beyond the availability of daylight. Numerical analysis of the thermal transient response of a thermocline storage tank is carried out for the charging phase. The system of equations describing the numerical model is solved by using time-implicit and space-backward finite differences and which encoded within the Matlab environment. The analysis presented the following findings: the predictions agree well with the experiments for the time evolution of the thermocline region, particularly for the regions away from the top-inlet. The deviations observed in the near-region of the inlet are most likely due to the high-level of turbulence in this region due to the localized level of mixing resulting; a simple analytical model to take into consideration this increased turbulence level was developed and it leads to some improvement of the predictions; this approach requires practically no additional computational effort and it relates the effective thermal diffusivity to the mean effective velocity of the fluid at each particular height of the system. Altogether the study indicates that the selected parabolic trough-CSP plant has the edge over alternative competing technologies for locations where DNI is high and where land usage is not an issue, such as the shoreline of Libya.

Modelling of an efficient dynamic smart solar photovoltaic power grid system

A smart solar photovoltaic grid system is an advent of innovation coherence of information and communications technology (ICT) with power systems control engineering via the internet [1]. This thesis designs and demonstrates a smart solar photovoltaic grid system that is selfhealing, environmental and consumer friendly, but also with the ability to accommodate other renewable sources of energy generation seamlessly, creating a healthy competitive energy industry and optimising energy assets efficiency. This thesis also presents the modelling of an efficient dynamic smart solar photovoltaic power grid system by exploring the maximum power point tracking efficiency, optimisation of the smart solar photovoltaic array through modelling and simulation to improve the quality of design for the solar photovoltaic module. In contrast, over the past decade quite promising results have been published in literature, most of which have not addressed the basis of the research questions in this thesis. The Levenberg-Marquardt and sparse based algorithms have proven to be very effective tools in helping to improve the quality of design for solar photovoltaic modules, minimising the possible relative errors in this thesis. Guided by theoretical and analytical reviews in literature, this research has carefully chosen the MatLab/Simulink software toolbox for modelling and simulation experiments performed on the static smart solar grid system. The auto-correlation coefficient results obtained from the modelling experiments give an accuracy of 99% with negligible mean square error (MSE), root mean square error (RMSE) and standard deviation. This thesis further explores the design and implementation of a robust real-time online solar photovoltaic monitoring system, establishing a comparative study of two solar photovoltaic tracking systems which provide remote access to the harvested energy data. This research made a landmark innovation in designing and implementing a unique approach for online remote access solar photovoltaic monitoring systems providing updated information of the energy produced by the solar photovoltaic module at the site location. In addressing the challenge of online solar photovoltaic monitoring systems, Darfon online data logger device has been systematically integrated into the design for a comparative study of the two solar photovoltaic tracking systems examined in this thesis. The site location for the comparative study of the solar photovoltaic tracking systems is at the National Kaohsiung University of Applied Sciences, Taiwan, R.O.C. The overall comparative energy output efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic monitoring system as observed at the research location site is about 72% based on the total energy produced, estimated money saved and the amount of CO2 reduction achieved. Similarly, in comparing the total amount of energy produced by the two solar photovoltaic tracking systems, the overall daily generated energy for the month of July shows the effectiveness of the azimuthal-altitude tracking systems over the 450 stationary solar photovoltaic system. It was found that the azimuthal-altitude dual-axis tracking systems were about 68.43% efficient compared to the 450 stationary solar photovoltaic systems. Lastly, the overall comparative hourly energy efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic energy system was found to be 74.2% efficient. Results from this research are quite promising and significant in satisfying the purpose of the research objectives and questions posed in the thesis. The new algorithms introduced in this research and the statistical measures applied to the modelling and simulation of a smart static solar photovoltaic grid system performance outperformed other previous works in reviewed literature. Based on this new implementation design of the online data logging systems for solar photovoltaic monitoring, it is possible for the first time to have online on-site information of the energy produced remotely, fault identification and rectification, maintenance and recovery time deployed as fast as possible. The results presented in this research as Internet of things (IoT) on smart solar grid systems are likely to offer real-life experiences especially both to the existing body of knowledge and the future solar photovoltaic energy industry irrespective of the study site location for the comparative solar photovoltaic tracking systems. While the thesis has contributed to the smart solar photovoltaic grid system, it has also highlighted areas of further research and the need to investigate more on improving the choice and quality design for solar photovoltaic modules. Finally, it has also made recommendations for further research in the minimization of the absolute or relative errors in the quality and design of the smart static solar photovoltaic module.